Using Deep Learning in Arabic-English Cross Language Information Retrieval

In this paper, we apply a combination of keyword-based information retrieval with a latent semantic-based model in Arabic-English Cross-Language Information Retrieval (CLIR). We aim at enabling Arabic-speaking users to access English content using their native language. Due to the complex morphology of Arabic, we use deep learning for both Arabic and English text to extract the deep semantics in the given languages and then use canonical correlation analysis (CCA) to project the semantic representations into a shared space in which retrieval can be done easily. We evaluate our system on selected articles from Wikipedia and show that the proposed combination can improve the state-of-the-art keyword-based CLIR performance.